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Face Recognition Algorithm Based on Fast Computation of Orthogonal Moments

Author

Listed:
  • Sadiq H. Abdulhussain

    (Department of Computer Engineering, University of Baghdad, Al-Jadriya, Baghdad 10071, Iraq
    These authors contributed equally to this work.)

  • Basheera M. Mahmmod

    (Department of Computer Engineering, University of Baghdad, Al-Jadriya, Baghdad 10071, Iraq
    These authors contributed equally to this work.)

  • Amer AlGhadhban

    (Electrical Engineering, College of Engineering, University of Ha’il, Ha’il 682507, Saudi Arabia
    These authors contributed equally to this work.)

  • Jan Flusser

    (Czech Academy of Sciences, Institute of Information Theory and Automation, Pod Vodárenskou vìží 4, 18208 Prague, Czech Republic
    Faculty of Management, University of Economics, Jarosovska 1117/II, 37701 Jindrichuv Hradec, Czech Republic
    These authors contributed equally to this work.)

Abstract

Face recognition is required in various applications, and major progress has been witnessed in this area. Many face recognition algorithms have been proposed thus far; however, achieving high recognition accuracy and low execution time remains a challenge. In this work, a new scheme for face recognition is presented using hybrid orthogonal polynomials to extract features. The embedded image kernel technique is used to decrease the complexity of feature extraction, then a support vector machine is adopted to classify these features. Moreover, a fast-overlapping block processing algorithm for feature extraction is used to reduce the computation time. Extensive evaluation of the proposed method was carried out on two different face image datasets, ORL and FEI. Different state-of-the-art face recognition methods were compared with the proposed method in order to evaluate its accuracy. We demonstrate that the proposed method achieves the highest recognition rate in different considered scenarios. Based on the obtained results, it can be seen that the proposed method is robust against noise and significantly outperforms previous approaches in terms of speed.

Suggested Citation

  • Sadiq H. Abdulhussain & Basheera M. Mahmmod & Amer AlGhadhban & Jan Flusser, 2022. "Face Recognition Algorithm Based on Fast Computation of Orthogonal Moments," Mathematics, MDPI, vol. 10(15), pages 1-28, August.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:15:p:2721-:d:877872
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    Citations

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    Cited by:

    1. Seng Chun Hoo & Haidi Ibrahim & Shahrel Azmin Suandi & Theam Foo Ng, 2023. "LCAM: Low-Complexity Attention Module for Lightweight Face Recognition Networks," Mathematics, MDPI, vol. 11(7), pages 1-27, April.
    2. Minghua Wan & Yuxi Zhang & Guowei Yang & Hongjian Guo, 2023. "Two-Dimensional Exponential Sparse Discriminant Local Preserving Projections," Mathematics, MDPI, vol. 11(7), pages 1-16, April.
    3. Ye Yuan & Jiahao Li & Qi Yu & Jian Liu & Zongdao Li & Qingdu Li & Na Liu, 2024. "A Two-Stage Facial Kinematic Control Strategy for Humanoid Robots Based on Keyframe Detection and Keypoint Cubic Spline Interpolation," Mathematics, MDPI, vol. 12(20), pages 1-16, October.

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